The straight-forward pixel-wise L2/Euclidian distance, equivalent to the mean squared error (MSE), is a popular choice for expressing differences between images [1]}, [2]}, [3]}, [4]}.
MSE has been used in the image reconstruction terms of loss functions for training several neural network approaches such as sparse autoencoders [1]}, variational autoencoders [2]}, convolutional autoencoders [3]}, and generative adversarial networks [4]}. In this work, we propose proximally sensitive error (PSE) as an alternative to MSE to express differences between images.
